
# Load data
import pandas as pd
df = pd.read_csv(r"C:\Users\Dell\OneDrive - Trinity College Dublin\Desktop\listings.csv")
df.head()
import sweetviz as sv
from IPython.core.display import display, HTML

# Analyze the dataframe
analyze_report = sv.analyze(df)

# Use display to view report directly in Jupyter Notebook
display(HTML(analyze_report.show_html('report.html', open_browser=False)))

C:\Users\Dell\AppData\Local\Temp\ipykernel_112\4252467769.py:3: DtypeWarning: Columns (68) have mixed types. Specify dtype option on import or set low_memory=False.
  df = pd.read_csv(r"C:\Users\Dell\OneDrive - Trinity College Dublin\Desktop\listings.csv")
C:\Users\Dell\AppData\Local\Temp\ipykernel_112\4252467769.py:6: DeprecationWarning: Importing display from IPython.core.display is deprecated since IPython 7.14, please import from IPython display
  from IPython.core.display import display, HTML

                                             |                                             | [  0%]   00:00 ->…

C:\Users\Dell\anaconda3\Lib\site-packages\sweetviz\series_analyzer.py:17: FutureWarning: The default dtype for empty Series will be 'object' instead of 'float64' in a future version. Specify a dtype explicitly to silence this warning.
  value_counts_without_nan = pd.Series()
C:\Users\Dell\anaconda3\Lib\site-packages\sweetviz\series_analyzer.py:17: FutureWarning: The default dtype for empty Series will be 'object' instead of 'float64' in a future version. Specify a dtype explicitly to silence this warning.
  value_counts_without_nan = pd.Series()
C:\Users\Dell\anaconda3\Lib\site-packages\sweetviz\series_analyzer.py:17: FutureWarning: The default dtype for empty Series will be 'object' instead of 'float64' in a future version. Specify a dtype explicitly to silence this warning.
  value_counts_without_nan = pd.Series()

Report report.html was generated.

<IPython.core.display.HTML object>


# Display the report directly in Jupyter Notebook
analyze_report.show_notebook()


df['price'] = df['price'].str.replace('$', '').str.replace(',', '').astype(float)

C:\Users\Dell\AppData\Local\Temp\ipykernel_112\1531488561.py:1: FutureWarning: The default value of regex will change from True to False in a future version. In addition, single character regular expressions will *not* be treated as literal strings when regex=True.
  df['price'] = df['price'].str.replace('$', '').str.replace(',', '').astype(float)


# Calculate average, minimum, maximum, median, and 3rd quartile prices for all of London
average_price = df['price'].mean()
min_price = df['price'].min()
max_price = df['price'].max()
median_price = df['price'].quantile(0.5)
third_quartile_price = df['price'].quantile(0.75)
print(f"Overall in London:")
print(f"Average Price: ${average_price:.2f}")
print(f"Minimum Price: ${min_price:.2f}")
print(f"Maximum Price: ${max_price:.2f}")
print(f"Median Price: ${median_price:.2f}")
print(f"3rd Quartile Price: ${third_quartile_price:.2f}")

Overall in London:
Average Price: $181.35
Minimum Price: $0.00
Maximum Price: $80100.00
Median Price: $110.00
3rd Quartile Price: $193.00


import pandas as pd
import matplotlib.pyplot as plt
import seaborn as sns

# Assuming df2 is your dataframe

# Calculate the IQR
Q1 = df['price'].quantile(0.25)
Q3 = df['price'].quantile(0.75)
IQR = Q3 - Q1

# Define bounds
lower_bound = Q1 - 1.5 * IQR
upper_bound = Q3 + 1.5 * IQR

# Filter out the outliers
df_no_outliers = df[(df['price'] >= lower_bound) & (df['price'] <= upper_bound)]

# Set style
sns.set_style("whitegrid")

# Creating a subplot of 1 row and 3 columns
fig, axes = plt.subplots(1, 3, figsize=(20, 5))

# Histogram
sns.histplot(df_no_outliers['price'], ax=axes[0], bins=30, kde=False, color='skyblue')
axes[0].set_title('Histogram of Prices' ,fontsize=17)
axes[0].set_xlabel('Price',fontsize=16)
axes[0].set_ylabel('Frequency',fontsize=16)
axes[0].tick_params(axis='both', labelsize=14)

# Boxplot
sns.boxplot(df_no_outliers['price'], ax=axes[1], color='lightgreen')
axes[1].set_title('Boxplot of Prices',fontsize=17)
axes[1].set_xlabel('Price',fontsize=16)
axes[1].tick_params(axis='both', labelsize=14)


# KDE
sns.kdeplot(df_no_outliers['price'], ax=axes[2], shade=True, color='salmon')
axes[2].set_title('Kernel Density Estimation of Prices',fontsize=17)
axes[2].set_xlabel('Price',fontsize=16)
axes[2].set_ylabel('Density',fontsize=16)
axes[2].tick_params(axis='both', labelsize=14)


plt.tight_layout()
plt.show()

C:\Users\Dell\AppData\Local\Temp\ipykernel_112\538630741.py:40: FutureWarning: 

`shade` is now deprecated in favor of `fill`; setting `fill=True`.
This will become an error in seaborn v0.14.0; please update your code.

  sns.kdeplot(df_no_outliers['price'], ax=axes[2], shade=True, color='salmon')


import pandas as pd
import matplotlib.pyplot as plt
import numpy as np
import seaborn as sns

# Sample data frame. Replace this with loading your dataset
# df2 = pd.read_csv('your_dataset_path.csv')

# Group data by neighbourhood_cleansed and calculate the average number_of_reviews and average price for each neighbourhood
grouped_data = df.groupby('neighbourhood_cleansed').agg({
    'number_of_reviews': 'mean',
    'price': 'mean'
}).sort_values(by='number_of_reviews', ascending=False)

# Normalize the average price values to create a colormap
norm = plt.Normalize(grouped_data['price'].min(), grouped_data['price'].max())
cmap = plt.get_cmap("YlOrRd")

# Color mapping
bar_colors = cmap(norm(grouped_data['price'].values))


# Color mapping
bar_colors = cmap(norm(grouped_data['price'].values))

# Visualization for Neighbourhood Popularity with bars colored by average price
fig, ax = plt.subplots(figsize=(10, 6))
bars = ax.bar(grouped_data.index, grouped_data['number_of_reviews'], color=bar_colors, width=0.6)  # Adjust the width for thinner bars
ax.set_title("Neighbourhoods by Average Number of Reviews")
ax.set_ylabel("Average Number of Reviews")
ax.set_xlabel("Neighbourhood")
plt.xticks(rotation=45, ha='right')

# Add a colorbar to the right
cbar = fig.colorbar(plt.cm.ScalarMappable(norm=norm, cmap=cmap), ax=ax, orientation='vertical', label='Average Price ($)')
plt.tight_layout()
plt.show()


import pandas as pd
import matplotlib.pyplot as plt
import seaborn as sns


# Group by room_type and calculate both average price and average number of reviews
grouped_data = df.groupby('room_type').agg({
    'price': 'mean',
    'number_of_reviews': 'mean'
}).reset_index()


import pandas as pd
import matplotlib.pyplot as plt
import seaborn as sns


# Group by room_type and calculate both average price and average number of reviews
grouped_data = df.groupby('room_type').agg({
    'price': 'mean',
    'number_of_reviews': 'mean'
}).reset_index()

# Visualization
plt.figure(figsize=(7, 5))

# Using seaborn's barplot for the grouped data
ax = sns.barplot(data=grouped_data, x='room_type', y='number_of_reviews', palette='viridis', hue='price', dodge=False)

# Formatting legend labels to have two decimal places
legend_labels, _ = ax.get_legend_handles_labels()
ax.legend(legend_labels, [f"${float(price):.2f}" for price in _], title='Average Price ($)')

plt.title("Popularity and Average Price based on Room Type")
plt.ylabel("Average Number of Reviews")
plt.xlabel("Room Type")
plt.tight_layout()
plt.show()


import pandas as pd
import seaborn as sns
import matplotlib.pyplot as plt



# Calculate the average availabilities
avg_availabilities = df.agg({
    'availability_30': 'mean',
    'availability_60': 'mean',
    'availability_90': 'mean',
    'availability_365': 'mean'
})


# Setting up the subplots
fig, axes = plt.subplots(nrows=1, ncols=2, figsize=(14, 5))

# Plot 1: Countplot for has_availability values on the first subplot
sns.countplot(data=df, x='has_availability', palette='viridis', ax=axes[0])
axes[0].set_title('Count of Listings by Availability Status',fontsize=15)
axes[0].set_ylabel('Number of Listings',fontsize=14)
axes[0].set_xlabel('Has Availability',fontsize=14)
axes[0].tick_params(axis='both', labelsize=14)
# Plot 2: Barplot for average availabilities on the second subplot
avg_availabilities.plot(kind='bar', color='c', ax=axes[1])
axes[1].set_title('Average Availability Over Different Durations',fontsize=15)
axes[1].set_ylabel('Average Days Available',fontsize=14)
axes[1].set_xlabel('Availability Duration',fontsize=14)
axes[1].tick_params(axis='x', rotation=0)  # Set rotation to 0 for x-axis labels
axes[1].tick_params(axis='both', labelsize=14)
# Adjusting for better legibility
axes[1].set_xticklabels(axes[1].get_xticklabels(), rotation=0, fontsize=10)

# Tight layout to ensure no overlap
plt.tight_layout()
plt.show()


#Printing availability
print(avg_availabilities)

availability_30       7.821663
availability_60      18.679974
availability_90      31.366680
availability_365    121.112808
dtype: float64


import pandas as pd
import seaborn as sns
import matplotlib.pyplot as plt

# If the listing is instantly bookable, replace "N/A" values in host_response_time to 'within an hour'
df.loc[(df['instant_bookable'] == 't') & (df['host_response_time'] == 'N/A'), 'host_response_time'] = 'within an hour'

# Grouping by host_response_time and calculating the average number_of_reviews
grouped_data = df.groupby('host_response_time')['number_of_reviews'].mean().reset_index()


# Visualization
plt.figure(figsize=(10, 6))
sns.barplot(data=grouped_data, x='host_response_time', y='number_of_reviews', palette='viridis')
plt.title('Impact of Host Response Time on Bookings')
plt.xlabel('Host Response Time')
plt.ylabel('Average Number of Reviews')
plt.xticks(rotation=45)
plt.tight_layout()
plt.show()

print(grouped_data)

   host_response_time  number_of_reviews
0  a few days or more           6.066195
1        within a day          14.098164
2  within a few hours          20.001935
3      within an hour          25.264985


import pandas as pd
import seaborn as sns
import matplotlib.pyplot as plt

# Grouping by host_identity_verified and calculating the average number_of_reviews and review_scores_rating
grouped_data = df.groupby('host_identity_verified').agg({
    'number_of_reviews': 'mean',
    'review_scores_rating': 'mean'
}).reset_index()


# Visualization
fig, ax = plt.subplots(nrows=1, ncols=2, figsize=(15, 6))

# Average number of reviews plot
sns.barplot(data=grouped_data, x='host_identity_verified', y='number_of_reviews', palette='viridis', ax=ax[0])
ax[0].set_title('Impact of Verification on Average Number of Reviews',fontsize=15)
ax[0].set_xlabel('Host Identity Verified',fontsize=14)
ax[0].set_ylabel('Average Number of Reviews',fontsize=14)
ax[0].tick_params(axis='both', labelsize=14)

# Average review_scores_rating plot
sns.barplot(data=grouped_data, x='host_identity_verified', y='review_scores_rating', palette='viridis', ax=ax[1])
ax[1].set_title('Impact of Verification on Average Review Score Rating',fontsize=15)
ax[1].set_xlabel('Host Identity Verified',fontsize=14)
ax[1].set_ylabel('Average Review Score Rating',fontsize=14)
ax[1].tick_params(axis='both', labelsize=14)

plt.tight_layout()
plt.show()

print(grouped_data)

  host_identity_verified  number_of_reviews  review_scores_rating
0                      f           6.871379              4.370692
1                      t          19.590804              4.621971


import pandas as pd

# Remove unwanted characters and split the amenities column by ","
amenities_series = df['amenities'].str.strip('[]').str.replace('"', '').str.split(', ')
amenities_count = amenities_series.explode().value_counts()


import seaborn as sns
import matplotlib.pyplot as plt

top_amenities = amenities_count.head(10)

plt.figure(figsize=(12, 6))
sns.barplot(y=top_amenities.index, x=top_amenities.values, palette='viridis')
plt.title('Top 10 Most Frequent Amenities')
plt.xlabel('Frequency')
plt.ylabel('Amenity')
plt.show()
print(amenities_count)

Kitchen                            80119
Wifi                               79856
Smoke alarm                        78620
Essentials                         73523
Iron                               62614
                                   ...  
Four  oven                             1
UE Boom Bluetooth sound system         1
Smeg  stainless steel gas stove        1
Electric hob  electric stove           1
Paul Mitchell shampoo                  1
Name: amenities, Length: 7291, dtype: int64


import pandas as pd
import seaborn as sns
import matplotlib.pyplot as plt

# Sample data
# df = pd.read_csv("your_dataset.csv")

# Convert price to float type (assuming it might be a string with currency symbols)
df['price'] = df['price'].replace('[\$,]', '', regex=True).astype(float)

# Calculate the number of amenities for each listing
# Assuming amenities are stored as a list or array-like structure in a string format
df['amenities_count'] = df['amenities'].str.split(',').str.len()

def filter_outliers(data, column_name):
    """Function to filter out outliers based on IQR for a given column."""
    Q1 = data[column_name].quantile(0.25)
    Q3 = data[column_name].quantile(0.75)
    IQR = Q3 - Q1
    return data[~((data[column_name] < (Q1 - 1.5 * IQR)) | (data[column_name] > (Q3 + 1.5 * IQR)))]

# Filter out outliers for both 'price' and 'amenities_count'
df_filtered = filter_outliers(df, 'price')
df_filtered = filter_outliers(df_filtered, 'amenities_count')


# Calculate the correlation between price and the number of amenities
correlation = df_filtered[['price', 'amenities_count']].corr().iloc[0, 1]
print(f"Correlation between price and number of amenities: {correlation}")

# Scatter plot with regression line
plt.figure(figsize=(10,6))
sns.regplot(x=df_filtered['amenities_count'], y=df_filtered['price'], scatter_kws={'s':10, 'alpha':0.5}, line_kws={'color':'red'})
plt.title('Scatter Plot with Regression Line of Number of Amenities vs Price (Outliers Removed)')
plt.xlabel('Number of Amenities')
plt.ylabel('Price')
plt.show()

Correlation between price and number of amenities: 0.29270030492598725


import folium
import branca

# Filter rows with price till 9th decile
price_threshold = df['price'].quantile(0.9)
df2_filtered = df[df['price'] <= price_threshold]


# Create a base map
m = folium.Map(location=[51.5074, -0.1278], zoom_start=10, tiles='cartodb positron')

# Define the mid-point for the color scale (median price)
min_price = df2_filtered['price'].min()
midpoint = df2_filtered['price'].median()
max_price = df2_filtered['price'].max()

# Create a diverging color map based on property prices
colormap = branca.colormap.LinearColormap(colors=['blue', 'yellow', 'red'],
                                          index=[min_price, midpoint, max_price],
                                          vmin=min_price, 
                                          vmax=max_price)

# Add CircleMarkers to the map with gradient colors based on price
for idx, row in df2_filtered.iterrows():
    folium.CircleMarker(location=(row['latitude'], row['longitude']),
                        radius=0.5,
                        color=colormap(row['price']),
                        fill=True,
                        fill_opacity=0.4).add_to(m)

# Add the color map legend to the map
colormap.caption = 'Property Prices'
colormap.add_to(m)

m


import pandas as pd
import xgboost as xgb

# Load the dataset
df_1 = pd.read_csv(r"C:\Users\Dell\OneDrive - Trinity College Dublin\Desktop\listings.csv")

Columns (68) have mixed types. Specify dtype option on import or set low_memory=False.


# Check the number of missing values per column
print(df_1[features].isnull().sum())

# Simple imputation using median for numeric features
features = ['number_of_reviews', 'availability_30', 'availability_60', 'availability_90', 'availability_365', 'beds', 'accommodates', 'bathrooms', 'minimum_nights', 'maximum_nights', 'review_scores_rating', 'review_scores_cleanliness', 'review_scores_location', 'review_scores_value', 'reviews_per_month', 'host_response_rate', 'host_acceptance_rate']

# Convert percentage columns to float
percentage_columns = ['host_response_rate', 'host_acceptance_rate']
for col in percentage_columns:
    df_1[col] = df_1[col].str.rstrip('%').astype('float') / 100.0
    
# Convert price column to float
df_1['price'] = df_1['price'].replace('[\$,]', '', regex=True).astype(float)

# Note: Customize this part based on domain knowledge or other considerations
for feature in features:
    df_1[feature] = df_1[feature].fillna(df_1[feature].median())

# If 'price' has missing values, we can drop or impute depending on its quantity
df_1['price'] = df_1['price'].fillna(df_1['price'].median())

X = df_1[features]
y = df_1['price']

from sklearn.model_selection import train_test_split
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)

model = xgb.XGBRegressor()
model.fit(X_train, y_train)

number_of_reviews                0
availability_30                  0
availability_60                  0
availability_90                  0
availability_365                 0
beds                          1134
accommodates                     0
bathrooms                    87946
minimum_nights                   0
maximum_nights                   0
review_scores_rating         22158
review_scores_cleanliness    23081
review_scores_location       23125
review_scores_value          23126
reviews_per_month            22158
host_response_rate           28918
host_acceptance_rate         25188
dtype: int64

XGBRegressor(base_score=None, booster=None, callbacks=None,
             colsample_bylevel=None, colsample_bynode=None,
             colsample_bytree=None, device=None, early_stopping_rounds=None,
             enable_categorical=False, eval_metric=None, feature_types=None,
             gamma=None, grow_policy=None, importance_type=None,
             interaction_constraints=None, learning_rate=None, max_bin=None,
             max_cat_threshold=None, max_cat_to_onehot=None,
             max_delta_step=None, max_depth=None, max_leaves=None,
             min_child_weight=None, missing=nan, monotone_constraints=None,
             multi_strategy=None, n_estimators=None, n_jobs=None,
             num_parallel_tree=None, random_state=None, ...)

XGBRegressor(base_score=None, booster=None, callbacks=None,
             colsample_bylevel=None, colsample_bynode=None,
             colsample_bytree=None, device=None, early_stopping_rounds=None,
             enable_categorical=False, eval_metric=None, feature_types=None,
             gamma=None, grow_policy=None, importance_type=None,
             interaction_constraints=None, learning_rate=None, max_bin=None,
             max_cat_threshold=None, max_cat_to_onehot=None,
             max_delta_step=None, max_depth=None, max_leaves=None,
             min_child_weight=None, missing=nan, monotone_constraints=None,
             multi_strategy=None, n_estimators=None, n_jobs=None,
             num_parallel_tree=None, random_state=None, ...)


from sklearn.metrics import mean_squared_error, mean_absolute_error, r2_score

# Predictions
y_pred = model.predict(X_test)

# Metrics
mse = mean_squared_error(y_test, y_pred)
rmse = mean_squared_error(y_test, y_pred, squared=False)
mae = mean_absolute_error(y_test, y_pred)
r2 = r2_score(y_test, y_pred)

print(f"Mean Squared Error: {mse}")
print(f"Root Mean Squared Error: {rmse}")
print(f"Mean Absolute Error: {mae}")
print(f"R2 Score: {r2}")

Mean Squared Error: 304258.59006906854
Root Mean Squared Error: 551.5964014286791
Mean Absolute Error: 96.04405228543631
R2 Score: 0.030200337452850445


import shap

# SHAP explainer
explainer = shap.Explainer(model)
shap_values = explainer(X_test)

# SHAP summary plot
shap.summary_plot(shap_values, X_test)

[15:37:44] WARNING: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-07f6e447eee219473-1\xgboost\xgboost-ci-windows\src\c_api\c_api.cc:1240: Saving into deprecated binary model format, please consider using `json` or `ubj`. Model format will default to JSON in XGBoost 2.2 if not specified.
[15:37:45] WARNING: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-07f6e447eee219473-1\xgboost\xgboost-ci-windows\src\c_api\c_api.cc:1240: Saving into deprecated binary model format, please consider using `json` or `ubj`. Model format will default to JSON in XGBoost 2.2 if not specified.
All-NaN slice encountered

Airbnb Data Analysis Project

Airbnb London Data Analysis (06 September, 2023)¶

Data Source¶

Data Loading and Initial Exploration using Sweetviz¶

Questions and Analysis¶

1. Pricing Strategy: What are the average, minimum, and maximum, median prices for listings in London¶

Preprocessing¶

Analysis¶

Answer¶

Business value¶

Question 2: Neighbourhood Popularity: Which neighbourhood is the most popular?¶

Preprocessing¶

Analysis¶

Answer: The City of London neighborhood has the highest number of reviews on average with an average price of around 250 dollars however there are other neighborhoods which are not as popular as City of London but more expensive¶

Question 3: Price and Popularity Trend Based on Property and Room Type: What is the average price of each room/property type¶

Preprocessing¶

Analysis¶

Answer:¶

Q4. Availability Trend Analysis: What is average availability of house based on 30,60,90 and 365 days.¶

Preprocessing¶

Analysis¶

Answer¶

Q5.Impact of Host Response Time on Bookings: Which category of response time has the highest number of reviews¶

Preprocessing¶

Analysis¶

Answer¶

Q6.Impact of Verification on Booking:¶

Preprocessing¶

Analysis¶

Answer¶

Q7. Frequency of Amenities:¶

Preprocessing¶

Analysis¶

Answer¶

Q8. What is the correlation between Price and number of Amenities:¶

Preprocessing¶

Analysis¶

Q9. What does the price distribution look like across the city map?¶

Preprocessing¶

Analysis¶

Key finding: Properties closer to city center are priced higher and properties in far off neighborhoods are relatively cheaper¶

Q10. Assesing the important factors in pricing strategy using shap values¶